{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KEN5RYXIPD4BASOW47KTHDTSVH","short_pith_number":"pith:KEN5RYXI","schema_version":"1.0","canonical_sha256":"511bd8e2e878f81049d6e7d5338e72a9e106fb9da367fa528520d8764beb9056","source":{"kind":"arxiv","id":"2410.02401","version":7},"attestation_state":"computed","paper":{"title":"SynCo: Synthetic Hard Negatives for Contrastive Visual Representation Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Nikolaos Giakoumoglou, Tania Stathaki","submitted_at":"2024-10-03T11:29:09Z","abstract_excerpt":"Contrastive learning has become a dominant approach in self-supervised visual representation learning, but efficiently leveraging hard negatives, which are samples closely resembling the anchor, remains challenging. We introduce SynCo (Synthetic negatives in Contrastive learning), a novel approach that improves model performance by generating synthetic hard negatives on the representation space. Building on the MoCo framework, SynCo introduces six strategies for creating diverse synthetic hard negatives on-the-fly with minimal computational overhead. SynCo achieves faster training and strong r"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.02401","kind":"arxiv","version":7},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-03T11:29:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1234f6118d3129245e484745379bbb909802d80e3acf8fc244790ca8fde23433","abstract_canon_sha256":"f3ebed42721d665b5c6650fd9caf4dc3f6a0d7801a0abed8f3652cfab38036f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:19.616937Z","signature_b64":"o2ETR/9uck3rCXyKeUhXAjui28/8jksiyf8H/WfCmxIokYDgLkyUcUNedqaJMDh1s+1YT0Fm9CPidNo4wrl6Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"511bd8e2e878f81049d6e7d5338e72a9e106fb9da367fa528520d8764beb9056","last_reissued_at":"2026-07-05T10:15:19.616426Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:19.616426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SynCo: Synthetic Hard Negatives for Contrastive Visual Representation Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Nikolaos Giakoumoglou, Tania Stathaki","submitted_at":"2024-10-03T11:29:09Z","abstract_excerpt":"Contrastive learning has become a dominant approach in self-supervised visual representation learning, but efficiently leveraging hard negatives, which are samples closely resembling the anchor, remains challenging. We introduce SynCo (Synthetic negatives in Contrastive learning), a novel approach that improves model performance by generating synthetic hard negatives on the representation space. Building on the MoCo framework, SynCo introduces six strategies for creating diverse synthetic hard negatives on-the-fly with minimal computational overhead. SynCo achieves faster training and strong r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02401","kind":"arxiv","version":7},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.02401/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.02401","created_at":"2026-07-05T10:15:19.616483+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.02401v7","created_at":"2026-07-05T10:15:19.616483+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02401","created_at":"2026-07-05T10:15:19.616483+00:00"},{"alias_kind":"pith_short_12","alias_value":"KEN5RYXIPD4B","created_at":"2026-07-05T10:15:19.616483+00:00"},{"alias_kind":"pith_short_16","alias_value":"KEN5RYXIPD4BASOW","created_at":"2026-07-05T10:15:19.616483+00:00"},{"alias_kind":"pith_short_8","alias_value":"KEN5RYXI","created_at":"2026-07-05T10:15:19.616483+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.26523","citing_title":"StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splitting","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KEN5RYXIPD4BASOW47KTHDTSVH","json":"https://pith.science/pith/KEN5RYXIPD4BASOW47KTHDTSVH.json","graph_json":"https://pith.science/api/pith-number/KEN5RYXIPD4BASOW47KTHDTSVH/graph.json","events_json":"https://pith.science/api/pith-number/KEN5RYXIPD4BASOW47KTHDTSVH/events.json","paper":"https://pith.science/paper/KEN5RYXI"},"agent_actions":{"view_html":"https://pith.science/pith/KEN5RYXIPD4BASOW47KTHDTSVH","download_json":"https://pith.science/pith/KEN5RYXIPD4BASOW47KTHDTSVH.json","view_paper":"https://pith.science/paper/KEN5RYXI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.02401&json=true","fetch_graph":"https://pith.science/api/pith-number/KEN5RYXIPD4BASOW47KTHDTSVH/graph.json","fetch_events":"https://pith.science/api/pith-number/KEN5RYXIPD4BASOW47KTHDTSVH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KEN5RYXIPD4BASOW47KTHDTSVH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KEN5RYXIPD4BASOW47KTHDTSVH/action/storage_attestation","attest_author":"https://pith.science/pith/KEN5RYXIPD4BASOW47KTHDTSVH/action/author_attestation","sign_citation":"https://pith.science/pith/KEN5RYXIPD4BASOW47KTHDTSVH/action/citation_signature","submit_replication":"https://pith.science/pith/KEN5RYXIPD4BASOW47KTHDTSVH/action/replication_record"}},"created_at":"2026-07-05T10:15:19.616483+00:00","updated_at":"2026-07-05T10:15:19.616483+00:00"}